发表机构
Bilkent University; University of Illinois Chicago; CentraleSupélec; Imperial College London（比尔肯特大学; 伊利诺伊大学芝加哥分校; 中央理工学院; 帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对带能量收集设备的异质性数据分布场景，提出统一集群感知空中联邦学习框架，可分别实现全局训练的公平性优化与模型个性化，同时降低通信开销。
AI 中文摘要
联邦学习（FL）支持在去中心化边缘设备间开展分布式优化与学习，同时保障数据隐私，但其性能受数据分布异质性、通信资源有限性及能量可用性的根本限制。在实际无线网络中，移动设备（MD）常呈现多样化数据与学习目标，自然形成可联合训练模型的用户集群。当设备依赖能量收集（EH）时，随机能量到达会在通信约束下进一步加剧参与和调度的复杂性。本研究针对带能量收集MD的空中（OTA）FL，在异质性数据分布下，于统一框架内研究两个紧密关联的学习目标：一是通过减少数据偏差获得更具代表性的全局模型，二是利用该偏差学习更个性化的集群特定模型。在全局训练模式下，集群信息指导感知能量与多样性的调度，确保被调度的活跃用户提供更具代表性的聚合更新；在个性化模式下，相同集群结构定义集群级学习目标与OTA恢复目标，使参数服务器可通过无线多址信道的同时传输训练多个集群特定模型。数值结果表明，所提统一框架依运行模式不同可提升公平性或个性化程度，同时降低通信开销。
英文摘要
Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability. In practical wireless networks, mobile devices (MDs) often exhibit diverse data and learning objectives, naturally forming clusters of users with jointly trainable models. When devices rely on energy harvesting (EH), stochastic energy arrivals further complicate participation and scheduling under communication constraints. In this work, we study over-the-air (OTA) FL with EH MDs under heterogeneous data distributions, and investigate two closely related learning objectives within a unified framework: one aiming for a more representative global model by reducing data bias, and the other learning more personalized cluster-specific models by exploiting this bias. In the global training mode, cluster information guides energy- and diversity-aware scheduling, ensuring that the scheduled active users provide a more representative aggregate update. In the personalization mode, the same cluster structure defines cluster-level learning objectives and OTA recovery targets, enabling the parameter server to train multiple cluster-specific models through simultaneous transmissions over the wireless multiple-access channel. Numerical results demonstrate that the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.
Comments17 pages
